Handbook of academic evaluation

xviii, 518 p.; 23 cm

Two Achehnese poems : Hikajat Ranto and hikajat Teungku di Meuke'

viii, 99 p; 23 cm

Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ~1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units.Publisher PDFPeer reviewe

Toward a universal model to calculate the solvation thermodynamics of druglike molecules : the importance of new experimental databases

We demonstrate that a new free energy functional in the integral equation theory of molecular liquids gives accurate calculations of hydration thermodynamics for drug-like molecules. The functional provides an improved description of excluded volume effects by incorporating two free coefficients. When the values of these coefficients are obtained from experimental data for simple organic molecules, the hydration free energies of an external test set of druglike molecules can be calculated with an accuracy of about 1 kcal/mol. The 3D RISM/UC method proposed here is easily implemented using existing computational software and allows in silico screening of the solvation thermodynamics of potential pharmaceutical molecules at significantly lower computational expense than explicit solvent simulations